Skip to content
GitLab
Menu
Projects
Groups
Snippets
Help
Help
Support
Community forum
Keyboard shortcuts
?
Submit feedback
Contribute to GitLab
Sign in
Toggle navigation
Menu
Open sidebar
Gregory Ashton
PyFstat
Commits
e976b57c
Commit
e976b57c
authored
Jul 01, 2019
by
Gregory Ashton
Browse files
Merge branch 'use-black-PEP8-codestyle' into 'master'
Use black pep8 codestyle See merge request
!20
parents
5c7e89a5
a2acfcff
Changes
30
Expand all
Hide whitespace changes
Inline
Side-by-side
examples/MCMC_examples/fully_coherent_search_using_MCMC.py
View file @
e976b57c
...
...
@@ -4,7 +4,7 @@ import numpy as np
# Properties of the GW data
sqrtSX
=
1e-23
tstart
=
1000000000
duration
=
100
*
86400
duration
=
100
*
86400
tend
=
tstart
+
duration
# Properties of the signal
...
...
@@ -13,39 +13,46 @@ F1 = -1e-10
F2
=
0
Alpha
=
np
.
radians
(
83.6292
)
Delta
=
np
.
radians
(
22.0144
)
tref
=
.
5
*
(
tstart
+
tend
)
tref
=
0
.5
*
(
tstart
+
tend
)
depth
=
10
h0
=
sqrtSX
/
depth
label
=
'
fully_coherent_search_using_MCMC
'
outdir
=
'
data
'
label
=
"
fully_coherent_search_using_MCMC
"
outdir
=
"
data
"
data
=
pyfstat
.
Writer
(
label
=
label
,
outdir
=
outdir
,
tref
=
tref
,
tstart
=
tstart
,
F0
=
F0
,
F1
=
F1
,
F2
=
F2
,
duration
=
duration
,
Alpha
=
Alpha
,
Delta
=
Delta
,
h0
=
h0
,
sqrtSX
=
sqrtSX
)
label
=
label
,
outdir
=
outdir
,
tref
=
tref
,
tstart
=
tstart
,
F0
=
F0
,
F1
=
F1
,
F2
=
F2
,
duration
=
duration
,
Alpha
=
Alpha
,
Delta
=
Delta
,
h0
=
h0
,
sqrtSX
=
sqrtSX
,
)
data
.
make_data
()
# The predicted twoF, given by lalapps_predictFstat can be accessed by
twoF
=
data
.
predict_fstat
()
print
(
'
Predicted twoF value: {}
\n
'
.
format
(
twoF
))
print
(
"
Predicted twoF value: {}
\n
"
.
format
(
twoF
))
DeltaF0
=
1e-7
DeltaF1
=
1e-13
VF0
=
(
np
.
pi
*
duration
*
DeltaF0
)
**
2
/
3.0
VF1
=
(
np
.
pi
*
duration
**
2
*
DeltaF1
)
**
2
*
4
/
45.
print
(
'
\n
V={:1.2e}, VF0={:1.2e}, VF1={:1.2e}
\n
'
.
format
(
VF0
*
VF1
,
VF0
,
VF1
))
VF0
=
(
np
.
pi
*
duration
*
DeltaF0
)
**
2
/
3.0
VF1
=
(
np
.
pi
*
duration
**
2
*
DeltaF1
)
**
2
*
4
/
45.
0
print
(
"
\n
V={:1.2e}, VF0={:1.2e}, VF1={:1.2e}
\n
"
.
format
(
VF0
*
VF1
,
VF0
,
VF1
))
theta_prior
=
{
'F0'
:
{
'type'
:
'unif'
,
'lower'
:
F0
-
DeltaF0
/
2.
,
'upper'
:
F0
+
DeltaF0
/
2.
},
'F1'
:
{
'type'
:
'unif'
,
'lower'
:
F1
-
DeltaF1
/
2.
,
'upper'
:
F1
+
DeltaF1
/
2.
},
'F2'
:
F2
,
'Alpha'
:
Alpha
,
'Delta'
:
Delta
}
theta_prior
=
{
"F0"
:
{
"type"
:
"unif"
,
"lower"
:
F0
-
DeltaF0
/
2.0
,
"upper"
:
F0
+
DeltaF0
/
2.0
},
"F1"
:
{
"type"
:
"unif"
,
"lower"
:
F1
-
DeltaF1
/
2.0
,
"upper"
:
F1
+
DeltaF1
/
2.0
},
"F2"
:
F2
,
"Alpha"
:
Alpha
,
"Delta"
:
Delta
,
}
ntemps
=
2
log10beta_min
=
-
0.5
...
...
@@ -53,13 +60,22 @@ nwalkers = 100
nsteps
=
[
300
,
300
]
mcmc
=
pyfstat
.
MCMCSearch
(
label
=
label
,
outdir
=
outdir
,
sftfilepattern
=
'{}/*{}*sft'
.
format
(
outdir
,
label
),
theta_prior
=
theta_prior
,
tref
=
tref
,
minStartTime
=
tstart
,
maxStartTime
=
tend
,
nsteps
=
nsteps
,
nwalkers
=
nwalkers
,
ntemps
=
ntemps
,
log10beta_min
=
log10beta_min
)
label
=
label
,
outdir
=
outdir
,
sftfilepattern
=
"{}/*{}*sft"
.
format
(
outdir
,
label
),
theta_prior
=
theta_prior
,
tref
=
tref
,
minStartTime
=
tstart
,
maxStartTime
=
tend
,
nsteps
=
nsteps
,
nwalkers
=
nwalkers
,
ntemps
=
ntemps
,
log10beta_min
=
log10beta_min
,
)
mcmc
.
transform_dictionary
=
dict
(
F0
=
dict
(
subtractor
=
F0
,
symbol
=
'$f-f^\mathrm{s}$'
),
F1
=
dict
(
subtractor
=
F1
,
symbol
=
'$\dot{f}-\dot{f}^\mathrm{s}$'
))
F0
=
dict
(
subtractor
=
F0
,
symbol
=
"$f-f^\mathrm{s}$"
),
F1
=
dict
(
subtractor
=
F1
,
symbol
=
"$\dot{f}-\dot{f}^\mathrm{s}$"
),
)
mcmc
.
run
()
mcmc
.
plot_corner
(
add_prior
=
True
)
mcmc
.
print_summary
()
examples/MCMC_examples/semi_coherent_search_using_MCMC.py
View file @
e976b57c
...
...
@@ -4,7 +4,7 @@ import numpy as np
# Properties of the GW data
sqrtSX
=
1e-23
tstart
=
1000000000
duration
=
100
*
86400
duration
=
100
*
86400
tend
=
tstart
+
duration
# Properties of the signal
...
...
@@ -13,39 +13,46 @@ F1 = -1e-10
F2
=
0
Alpha
=
np
.
radians
(
83.6292
)
Delta
=
np
.
radians
(
22.0144
)
tref
=
.
5
*
(
tstart
+
tend
)
tref
=
0
.5
*
(
tstart
+
tend
)
depth
=
10
h0
=
sqrtSX
/
depth
label
=
'
semicoherent_search_using_MCMC
'
outdir
=
'
data
'
label
=
"
semicoherent_search_using_MCMC
"
outdir
=
"
data
"
data
=
pyfstat
.
Writer
(
label
=
label
,
outdir
=
outdir
,
tref
=
tref
,
tstart
=
tstart
,
F0
=
F0
,
F1
=
F1
,
F2
=
F2
,
duration
=
duration
,
Alpha
=
Alpha
,
Delta
=
Delta
,
h0
=
h0
,
sqrtSX
=
sqrtSX
)
label
=
label
,
outdir
=
outdir
,
tref
=
tref
,
tstart
=
tstart
,
F0
=
F0
,
F1
=
F1
,
F2
=
F2
,
duration
=
duration
,
Alpha
=
Alpha
,
Delta
=
Delta
,
h0
=
h0
,
sqrtSX
=
sqrtSX
,
)
data
.
make_data
()
# The predicted twoF, given by lalapps_predictFstat can be accessed by
twoF
=
data
.
predict_fstat
()
print
(
'
Predicted twoF value: {}
\n
'
.
format
(
twoF
))
print
(
"
Predicted twoF value: {}
\n
"
.
format
(
twoF
))
DeltaF0
=
1e-7
DeltaF1
=
1e-13
VF0
=
(
np
.
pi
*
duration
*
DeltaF0
)
**
2
/
3.0
VF1
=
(
np
.
pi
*
duration
**
2
*
DeltaF1
)
**
2
*
4
/
45.
print
(
'
\n
V={:1.2e}, VF0={:1.2e}, VF1={:1.2e}
\n
'
.
format
(
VF0
*
VF1
,
VF0
,
VF1
))
VF0
=
(
np
.
pi
*
duration
*
DeltaF0
)
**
2
/
3.0
VF1
=
(
np
.
pi
*
duration
**
2
*
DeltaF1
)
**
2
*
4
/
45.
0
print
(
"
\n
V={:1.2e}, VF0={:1.2e}, VF1={:1.2e}
\n
"
.
format
(
VF0
*
VF1
,
VF0
,
VF1
))
theta_prior
=
{
'F0'
:
{
'type'
:
'unif'
,
'lower'
:
F0
-
DeltaF0
/
2.
,
'upper'
:
F0
+
DeltaF0
/
2.
},
'F1'
:
{
'type'
:
'unif'
,
'lower'
:
F1
-
DeltaF1
/
2.
,
'upper'
:
F1
+
DeltaF1
/
2.
},
'F2'
:
F2
,
'Alpha'
:
Alpha
,
'Delta'
:
Delta
}
theta_prior
=
{
"F0"
:
{
"type"
:
"unif"
,
"lower"
:
F0
-
DeltaF0
/
2.0
,
"upper"
:
F0
+
DeltaF0
/
2.0
},
"F1"
:
{
"type"
:
"unif"
,
"lower"
:
F1
-
DeltaF1
/
2.0
,
"upper"
:
F1
+
DeltaF1
/
2.0
},
"F2"
:
F2
,
"Alpha"
:
Alpha
,
"Delta"
:
Delta
,
}
ntemps
=
1
log10beta_min
=
-
1
...
...
@@ -53,14 +60,23 @@ nwalkers = 100
nsteps
=
[
300
,
300
]
mcmc
=
pyfstat
.
MCMCSemiCoherentSearch
(
label
=
label
,
outdir
=
outdir
,
nsegs
=
10
,
sftfilepattern
=
'{}/*{}*sft'
.
format
(
outdir
,
label
),
theta_prior
=
theta_prior
,
tref
=
tref
,
minStartTime
=
tstart
,
maxStartTime
=
tend
,
nsteps
=
nsteps
,
nwalkers
=
nwalkers
,
ntemps
=
ntemps
,
log10beta_min
=
log10beta_min
)
label
=
label
,
outdir
=
outdir
,
nsegs
=
10
,
sftfilepattern
=
"{}/*{}*sft"
.
format
(
outdir
,
label
),
theta_prior
=
theta_prior
,
tref
=
tref
,
minStartTime
=
tstart
,
maxStartTime
=
tend
,
nsteps
=
nsteps
,
nwalkers
=
nwalkers
,
ntemps
=
ntemps
,
log10beta_min
=
log10beta_min
,
)
mcmc
.
transform_dictionary
=
dict
(
F0
=
dict
(
subtractor
=
F0
,
symbol
=
'$f-f^\mathrm{s}$'
),
F1
=
dict
(
subtractor
=
F1
,
symbol
=
'$\dot{f}-\dot{f}^\mathrm{s}$'
))
F0
=
dict
(
subtractor
=
F0
,
symbol
=
"$f-f^\mathrm{s}$"
),
F1
=
dict
(
subtractor
=
F1
,
symbol
=
"$\dot{f}-\dot{f}^\mathrm{s}$"
),
)
mcmc
.
run
()
mcmc
.
plot_corner
(
add_prior
=
True
)
mcmc
.
print_summary
()
examples/followup_examples/semi_coherent_directed_follow_up.py
View file @
e976b57c
...
...
@@ -11,39 +11,47 @@ Delta = np.radians(22.0144)
# Properties of the GW data
sqrtSX
=
1e-23
tstart
=
1000000000
duration
=
100
*
86400
tend
=
tstart
+
duration
tref
=
.
5
*
(
tstart
+
tend
)
duration
=
100
*
86400
tend
=
tstart
+
duration
tref
=
0
.5
*
(
tstart
+
tend
)
depth
=
40
label
=
'
semicoherent_directed_follow_up
'
outdir
=
'
data
'
label
=
"
semicoherent_directed_follow_up
"
outdir
=
"
data
"
h0
=
sqrtSX
/
depth
data
=
pyfstat
.
Writer
(
label
=
label
,
outdir
=
outdir
,
tref
=
tref
,
tstart
=
tstart
,
F0
=
F0
,
F1
=
F1
,
F2
=
F2
,
duration
=
duration
,
Alpha
=
Alpha
,
Delta
=
Delta
,
h0
=
h0
,
sqrtSX
=
sqrtSX
)
label
=
label
,
outdir
=
outdir
,
tref
=
tref
,
tstart
=
tstart
,
F0
=
F0
,
F1
=
F1
,
F2
=
F2
,
duration
=
duration
,
Alpha
=
Alpha
,
Delta
=
Delta
,
h0
=
h0
,
sqrtSX
=
sqrtSX
,
)
data
.
make_data
()
# The predicted twoF, given by lalapps_predictFstat can be accessed by
twoF
=
data
.
predict_fstat
()
print
(
'
Predicted twoF value: {}
\n
'
.
format
(
twoF
))
print
(
"
Predicted twoF value: {}
\n
"
.
format
(
twoF
))
# Search
VF0
=
VF1
=
1e5
DeltaF0
=
np
.
sqrt
(
VF0
)
*
np
.
sqrt
(
3
)
/
(
np
.
pi
*
duration
)
DeltaF1
=
np
.
sqrt
(
VF1
)
*
np
.
sqrt
(
180
)
/
(
np
.
pi
*
duration
**
2
)
theta_prior
=
{
'F0'
:
{
'type'
:
'unif'
,
'lower'
:
F0
-
DeltaF0
/
2.
,
'upper'
:
F0
+
DeltaF0
/
2
},
'F1'
:
{
'type'
:
'unif'
,
'lower'
:
F1
-
DeltaF1
/
2.
,
'upper'
:
F1
+
DeltaF1
/
2
},
'F2'
:
F2
,
'Alpha'
:
Alpha
,
'Delta'
:
Delta
}
DeltaF0
=
np
.
sqrt
(
VF0
)
*
np
.
sqrt
(
3
)
/
(
np
.
pi
*
duration
)
DeltaF1
=
np
.
sqrt
(
VF1
)
*
np
.
sqrt
(
180
)
/
(
np
.
pi
*
duration
**
2
)
theta_prior
=
{
"F0"
:
{
"type"
:
"unif"
,
"lower"
:
F0
-
DeltaF0
/
2.0
,
"upper"
:
F0
+
DeltaF0
/
2
},
"F1"
:
{
"type"
:
"unif"
,
"lower"
:
F1
-
DeltaF1
/
2.0
,
"upper"
:
F1
+
DeltaF1
/
2
},
"F2"
:
F2
,
"Alpha"
:
Alpha
,
"Delta"
:
Delta
,
}
ntemps
=
3
log10beta_min
=
-
0.5
...
...
@@ -51,23 +59,35 @@ nwalkers = 100
nsteps
=
[
100
,
100
]
mcmc
=
pyfstat
.
MCMCFollowUpSearch
(
label
=
label
,
outdir
=
outdir
,
sftfilepattern
=
'{}/*{}*sft'
.
format
(
outdir
,
label
),
theta_prior
=
theta_prior
,
tref
=
tref
,
minStartTime
=
tstart
,
maxStartTime
=
tend
,
nwalkers
=
nwalkers
,
nsteps
=
nsteps
,
ntemps
=
ntemps
,
log10beta_min
=
log10beta_min
)
label
=
label
,
outdir
=
outdir
,
sftfilepattern
=
"{}/*{}*sft"
.
format
(
outdir
,
label
),
theta_prior
=
theta_prior
,
tref
=
tref
,
minStartTime
=
tstart
,
maxStartTime
=
tend
,
nwalkers
=
nwalkers
,
nsteps
=
nsteps
,
ntemps
=
ntemps
,
log10beta_min
=
log10beta_min
,
)
NstarMax
=
1000
Nsegs0
=
100
fig
,
axes
=
plt
.
subplots
(
nrows
=
2
,
figsize
=
(
3.4
,
3.5
))
fig
,
axes
=
mcmc
.
run
(
NstarMax
=
NstarMax
,
Nsegs0
=
Nsegs0
,
labelpad
=
0.01
,
plot_det_stat
=
False
,
return_fig
=
True
,
fig
=
fig
,
axes
=
axes
)
NstarMax
=
NstarMax
,
Nsegs0
=
Nsegs0
,
labelpad
=
0.01
,
plot_det_stat
=
False
,
return_fig
=
True
,
fig
=
fig
,
axes
=
axes
,
)
for
ax
in
axes
:
ax
.
grid
()
ax
.
set_xticks
(
np
.
arange
(
0
,
600
,
100
))
ax
.
set_xticklabels
([
str
(
s
)
for
s
in
np
.
arange
(
0
,
700
,
100
)])
axes
[
-
1
].
set_xlabel
(
r
'
$\textrm{Number of steps}$
'
,
labelpad
=
0.1
)
axes
[
-
1
].
set_xlabel
(
r
"
$\textrm{Number of steps}$
"
,
labelpad
=
0.1
)
fig
.
tight_layout
()
fig
.
savefig
(
'
{}/{}_walkers.png
'
.
format
(
mcmc
.
outdir
,
mcmc
.
label
),
dpi
=
400
)
fig
.
savefig
(
"
{}/{}_walkers.png
"
.
format
(
mcmc
.
outdir
,
mcmc
.
label
),
dpi
=
400
)
examples/glitch_examples/make_simulated_data.py
View file @
e976b57c
from
pyfstat
import
Writer
,
GlitchWriter
import
numpy
as
np
outdir
=
'
data
'
outdir
=
"
data
"
# First, we generate data with a reasonably strong smooth signal
# Define parameters of the Crab pulsar as an example
...
...
@@ -17,37 +17,75 @@ h0 = 5e-24
# Properties of the GW data
sqrtSX
=
1e-22
tstart
=
1000000000
duration
=
50
*
86400
tend
=
tstart
+
duration
tref
=
tstart
+
0.5
*
duration
duration
=
50
*
86400
tend
=
tstart
+
duration
tref
=
tstart
+
0.5
*
duration
data
=
Writer
(
label
=
'0_glitch'
,
outdir
=
outdir
,
tref
=
tref
,
tstart
=
tstart
,
F0
=
F0
,
F1
=
F1
,
F2
=
F2
,
duration
=
duration
,
Alpha
=
Alpha
,
Delta
=
Delta
,
h0
=
h0
,
sqrtSX
=
sqrtSX
)
label
=
"0_glitch"
,
outdir
=
outdir
,
tref
=
tref
,
tstart
=
tstart
,
F0
=
F0
,
F1
=
F1
,
F2
=
F2
,
duration
=
duration
,
Alpha
=
Alpha
,
Delta
=
Delta
,
h0
=
h0
,
sqrtSX
=
sqrtSX
,
)
data
.
make_data
()
# Next, taking the same signal parameters, we include a glitch half way through
dtglitch
=
duration
/
2.0
dtglitch
=
duration
/
2.0
delta_F0
=
5e-6
delta_F1
=
0
glitch_data
=
GlitchWriter
(
label
=
'1_glitch'
,
outdir
=
outdir
,
tref
=
tref
,
tstart
=
tstart
,
F0
=
F0
,
F1
=
F1
,
F2
=
F2
,
duration
=
duration
,
Alpha
=
Alpha
,
Delta
=
Delta
,
h0
=
h0
,
sqrtSX
=
sqrtSX
,
dtglitch
=
dtglitch
,
delta_F0
=
delta_F0
,
delta_F1
=
delta_F1
)
label
=
"1_glitch"
,
outdir
=
outdir
,
tref
=
tref
,
tstart
=
tstart
,
F0
=
F0
,
F1
=
F1
,
F2
=
F2
,
duration
=
duration
,
Alpha
=
Alpha
,
Delta
=
Delta
,
h0
=
h0
,
sqrtSX
=
sqrtSX
,
dtglitch
=
dtglitch
,
delta_F0
=
delta_F0
,
delta_F1
=
delta_F1
,
)
glitch_data
.
make_data
()
# Making data with two glitches
dtglitch_2
=
[
duration
/
4.0
,
4
*
duration
/
5.0
]
dtglitch_2
=
[
duration
/
4.0
,
4
*
duration
/
5.0
]
delta_phi_2
=
[
0
,
0
]
delta_F0_2
=
[
4e-6
,
3e-7
]
delta_F1_2
=
[
0
,
0
]
delta_F2_2
=
[
0
,
0
]
two_glitch_data
=
GlitchWriter
(
label
=
'2_glitch'
,
outdir
=
outdir
,
tref
=
tref
,
tstart
=
tstart
,
F0
=
F0
,
F1
=
F1
,
F2
=
F2
,
duration
=
duration
,
Alpha
=
Alpha
,
Delta
=
Delta
,
h0
=
h0
,
sqrtSX
=
sqrtSX
,
dtglitch
=
dtglitch_2
,
delta_phi
=
delta_phi_2
,
delta_F0
=
delta_F0_2
,
delta_F1
=
delta_F1_2
,
delta_F2
=
delta_F2_2
)
label
=
"2_glitch"
,
outdir
=
outdir
,
tref
=
tref
,
tstart
=
tstart
,
F0
=
F0
,
F1
=
F1
,
F2
=
F2
,
duration
=
duration
,
Alpha
=
Alpha
,
Delta
=
Delta
,
h0
=
h0
,
sqrtSX
=
sqrtSX
,
dtglitch
=
dtglitch_2
,
delta_phi
=
delta_phi_2
,
delta_F0
=
delta_F0_2
,
delta_F1
=
delta_F1_2
,
delta_F2
=
delta_F2_2
,
)
two_glitch_data
.
make_data
()
examples/glitch_examples/semicoherent_glitch_robust_directed_MCMC_search_on_1_glitch.py
View file @
e976b57c
...
...
@@ -3,34 +3,42 @@ import matplotlib.pyplot as plt
import
pyfstat
import
gridcorner
import
time
from
make_simulated_data
import
tstart
,
duration
,
tref
,
F0
,
F1
,
F2
,
Alpha
,
Delta
,
delta_F0
,
dtglitch
,
outdir
from
make_simulated_data
import
(
tstart
,
duration
,
tref
,
F0
,
F1
,
F2
,
Alpha
,
Delta
,
delta_F0
,
dtglitch
,
outdir
,
)
plt
.
style
.
use
(
'
./paper.mplstyle
'
)
plt
.
style
.
use
(
"
./paper.mplstyle
"
)
label
=
'
semicoherent_glitch_robust_directed_MCMC_search_on_1_glitch
'
label
=
"
semicoherent_glitch_robust_directed_MCMC_search_on_1_glitch
"
Nstar
=
1000
F0_width
=
np
.
sqrt
(
Nstar
)
*
np
.
sqrt
(
12
)
/
(
np
.
pi
*
duration
)
F1_width
=
np
.
sqrt
(
Nstar
)
*
np
.
sqrt
(
180
)
/
(
np
.
pi
*
duration
**
2
)
F0_width
=
np
.
sqrt
(
Nstar
)
*
np
.
sqrt
(
12
)
/
(
np
.
pi
*
duration
)
F1_width
=
np
.
sqrt
(
Nstar
)
*
np
.
sqrt
(
180
)
/
(
np
.
pi
*
duration
**
2
)
theta_prior
=
{
'F0'
:
{
'type'
:
'unif'
,
'lower'
:
F0
-
F0_width
/
2.
,
'upper'
:
F0
+
F0_width
/
2.
},
'F1'
:
{
'type'
:
'unif'
,
'lower'
:
F1
-
F1_width
/
2.
,
'upper'
:
F1
+
F1_width
/
2.
},
'F2'
:
F2
,
'delta_F0'
:
{
'type'
:
'unif'
,
'lower'
:
0
,
'upper'
:
1e-5
},
'delta_F1'
:
0
,
'tglitch'
:
{
'type'
:
'unif'
,
'lower'
:
tstart
+
0.1
*
duration
,
'upper'
:
tstart
+
0.9
*
duration
},
'Alpha'
:
Alpha
,
'Delta'
:
Delta
,
}
"F0"
:
{
"type"
:
"unif"
,
"lower"
:
F0
-
F0_width
/
2.0
,
"upper"
:
F0
+
F0_width
/
2.0
},
"F1"
:
{
"type"
:
"unif"
,
"lower"
:
F1
-
F1_width
/
2.0
,
"upper"
:
F1
+
F1_width
/
2.0
},
"F2"
:
F2
,
"delta_F0"
:
{
"type"
:
"unif"
,
"lower"
:
0
,
"upper"
:
1e-5
},
"delta_F1"
:
0
,
"tglitch"
:
{
"type"
:
"unif"
,
"lower"
:
tstart
+
0.1
*
duration
,
"upper"
:
tstart
+
0.9
*
duration
,
},
"Alpha"
:
Alpha
,
"Delta"
:
Delta
,
}
ntemps
=
3
log10beta_min
=
-
0.5
...
...
@@ -38,33 +46,49 @@ nwalkers = 100
nsteps
=
[
250
,
250
]
mcmc
=
pyfstat
.
MCMCGlitchSearch
(
label
=
label
,
sftfilepattern
=
'data/*1_glitch*sft'
,
theta_prior
=
theta_prior
,
tref
=
tref
,
minStartTime
=
tstart
,
maxStartTime
=
tstart
+
duration
,
nsteps
=
nsteps
,
nwalkers
=
nwalkers
,
ntemps
=
ntemps
,
log10beta_min
=
log10beta_min
,
nglitch
=
1
)
mcmc
.
transform_dictionary
[
'F0'
]
=
dict
(
subtractor
=
F0
,
multiplier
=
1e6
,
symbol
=
'$f-f_\mathrm{s}$'
)
mcmc
.
unit_dictionary
[
'F0'
]
=
'$\mu$Hz'
mcmc
.
transform_dictionary
[
'F1'
]
=
dict
(
subtractor
=
F1
,
multiplier
=
1e12
,
symbol
=
'$\dot{f}-\dot{f}_\mathrm{s}$'
)
mcmc
.
unit_dictionary
[
'F1'
]
=
'$p$Hz/s'
mcmc
.
transform_dictionary
[
'delta_F0'
]
=
dict
(
multiplier
=
1e6
,
subtractor
=
delta_F0
,
symbol
=
'$\delta f-\delta f_\mathrm{s}$'
)
mcmc
.
unit_dictionary
[
'delta_F0'
]
=
'$\mu$Hz/s'
mcmc
.
transform_dictionary
[
'tglitch'
][
'subtractor'
]
=
tstart
+
dtglitch
mcmc
.
transform_dictionary
[
'tglitch'
][
'label'
]
=
'$t^\mathrm{g}-t^\mathrm{g}_\mathrm{s}$
\n
[d]'
label
=
label
,
sftfilepattern
=
"data/*1_glitch*sft"
,
theta_prior
=
theta_prior
,
tref
=
tref
,
minStartTime
=
tstart
,
maxStartTime
=
tstart
+
duration
,
nsteps
=
nsteps
,
nwalkers
=
nwalkers
,
ntemps
=
ntemps
,
log10beta_min
=
log10beta_min
,
nglitch
=
1
,
)
mcmc
.
transform_dictionary
[
"F0"
]
=
dict
(
subtractor
=
F0
,
multiplier
=
1e6
,
symbol
=
"$f-f_\mathrm{s}$"
)
mcmc
.
unit_dictionary
[
"F0"
]
=
"$\mu$Hz"
mcmc
.
transform_dictionary
[
"F1"
]
=
dict
(
subtractor
=
F1
,
multiplier
=
1e12
,
symbol
=
"$\dot{f}-\dot{f}_\mathrm{s}$"
)
mcmc
.
unit_dictionary
[
"F1"
]
=
"$p$Hz/s"
mcmc
.
transform_dictionary
[
"delta_F0"
]
=
dict
(
multiplier
=
1e6
,
subtractor
=
delta_F0
,
symbol
=
"$\delta f-\delta f_\mathrm{s}$"
)
mcmc
.
unit_dictionary
[
"delta_F0"
]
=
"$\mu$Hz/s"
mcmc
.
transform_dictionary
[
"tglitch"
][
"subtractor"
]
=
tstart
+
dtglitch
mcmc
.
transform_dictionary
[
"tglitch"
][
"label"
]
=
"$t^\mathrm{g}-t^\mathrm{g}_\mathrm{s}$
\n
[d]"
t1
=
time
.
time
()
mcmc
.
run
()
dT
=
time
.
time
()
-
t1
fig_and_axes
=
gridcorner
.
_get_fig_and_axes
(
4
,
2
,
0.05
)
mcmc
.
plot_corner
(
label_offset
=
0.25
,
truths
=
[
0
,
0
,
0
,
0
],
fig_and_axes
=
fig_and_axes
,
quantiles
=
(
0.16
,
0.84
),
hist_kwargs
=
dict
(
lw
=
1.5
,
zorder
=-
1
),
truth_color
=
'C3'
)
mcmc
.
plot_corner
(
label_offset
=
0.25
,
truths
=
[
0
,
0
,
0
,
0
],
fig_and_axes
=
fig_and_axes
,
quantiles
=
(
0.16
,
0.84
),
hist_kwargs
=
dict
(
lw
=
1.5
,
zorder
=-
1
),
truth_color
=
"C3"
,
)
mcmc
.
print_summary
()
print
((
'
Prior widths =
'
,
F0_width
,
F1_width
))
print
((
"
Prior widths =
"
,
F0_width
,
F1_width
))
print
((
"Actual run time = {}"
.
format
(
dT
)))
examples/glitch_examples/semicoherent_glitch_robust_directed_grid_search_on_1_glitch.py
View file @
e976b57c
import
pyfstat
import
numpy
as
np
import
matplotlib.pyplot
as
plt
from
make_simulated_data
import
tstart
,
duration
,
tref
,
F0
,
F1
,
F2
,
Alpha
,
Delta
,
delta_F0
,
outdir
,
dtglitch
from
make_simulated_data
import
(
tstart
,
duration
,
tref
,